#DuboisChallenge2026 Plate 2
#matplotlib #geopandas #pyfonts #dataviz

This week #TidyTuesday explores the agricultural production statistics in New Zealand. I created slope charts to show how the cultivated area of specific crops changed between two decades.
#pydytuesday #matplotlib #dataviz

I created a map of Corvo Island using osmnx library, showing natural features, land use, waterways and main roads, along with some key facts about the island.
#osmnx #matplotlib #pyfonts #dataviz #geospatial

This week #TidyTuesday explores the 2026 Winter Olympics. I divided the discipline names into groups and created 2D kernel density estimate subplots for each, showing how event day and duration are distributed together.
#pydytuesday #matplotlib #seaborn #dataviz

This week #TidyTuesday explores the Edible Plants Database. I converted the categorical measurements into numerical values, computed the average for each category, and plotted them as triangles inside circles with circle angles corresponding to the measurements.
#pydytuesday #matplotlib #dataviz

Corrected the 1e4 y label.

This week #TidyTuesday explores Brazilian Companies. I created a connected scatter plot of high and low capital stock pairs by legal nature. Axes are logarithmic to handle skewness, but labels display the original values.
#pydytuesday #matplotlib #dataviz

This is a great chart! I love the colors and the way it’s laid out.

This week #TidyTuesday explores the Astronomy Picture of the Day (APOD) archive. I filtered the data to focus on Portugal related images and visualized them using circle packing.
#pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores the Languages of Africa. I created radial bar charts for the top 3 language families.
#pydytuesday #matplotlib #dataviz

This week #TidyTuesday is about bringing our own data. Using the 2021 Census geopackage data from Portugal’s national statistics agency, I created a bivariate map of Lisbon’s residences categorized by low and high numbers of divisions.
#pydytuesday #matplotlib #geopandas #dataviz

Rest in peace Béla Tarr

Thanks! Let me know if you have any feedback!

This week #TidyTuesday explores Christmas novels from Project Gutenberg. I collected adjectives that directly precede the word “christmas” using part of speech tagging. Then I created an edge colormap graph to symbolize a sparkler.
#pydytuesday #matplotlib #spacy #networkx #dataviz

Happy holidays to you all!
#matplotlib #dataviz

This week #TidyTuesday explores the Languages of the World. I made some chord diagram subplots where each subplot highlights a different macro area.
#pydytuesday #matplotlib #pycirclize #dataviz

Yes, it seems incomplete. According to Kittelson's website (the curator of the database), there was a time when people could update the data, and a lot of junk was added. Perhaps after that, a lot was removed.

This week #TidyTuesday explores roundabouts in the world. I filtered the data for Portugal, added extra info, and made a grid of circles that looks like the Portugal map. The colors show how many roundabouts are in each district or autonomous region.
#pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores data about cars in Qatar. I visualized the distribution of car performance by manufacturing country using raincloud plots.
#pydytuesday #matplotlib #seaborn #dataviz

This week #TidyTuesday explores the weather prediction of Zurich's exploding snowman. I gathered additional data and created a timeline plot with some fun facts and weather prediction.
#pydytuesday #matplotlib #dataviz

I appreciate that, but I’m sure you did better than you’re giving yourself credit for.

Although I didn’t join the 30DayMapChallenge, but it still got me in the mood to plot some city grids using the osmnx library.
#osmnx #matplotlib #pyfonts #dataviz

This week #TidyTuesday explores the statistical performance indicators (SPI). I created gauge charts to show the overall statistical performance score per region.
#pydytuesday #dataviz #matplotlib

This week #TidyTuesday explores the Complete Sherlock Holmes. I selected a novel to compare how often Holmes and Watson are mentioned. After that I created square heatmaps to show the frequency of each mentioned name.
#pydytuesday #dataviz

Libby, thank you! That means a lot! I'm happy to have started participating in Tidytuesday. It's been an amazing learning journey, and it’s inspiring to see what everyone comes up with week by week. I’m thankful for your follow and support!

This week #TidyTuesday explores the global tuberculosis (TB) burden estimates. I used the great_tables library again, but this time I tried it with nanoplots.
#pydytuesday #dataviz

Congratulations!

This week #TidyTuesday explores the lead levels in water samples collected in Flint, Michigan in 2015. I created waffle charts to show the % of samples from MDEQ and Virginia Tech that are above or below the 15 ppb threshold. The 90th percentiles are also highlighted.
#pydytuesday #dataviz

Same here, adding it to my paranoia list too.

This week #TidyTuesday explores the selected British literary prizes data. I created area and line charts by selected genders to show how the number of writers changed over time and added some summary metrics.
#pydytuesday #dataviz